smooth.spline {modreg} | R Documentation

## Fit a Smoothing Spline

### Description

Fits a cubic smoothing spline to the supplied data.

### Usage

    smooth.spline(x, y = NULL, w = NULL, df, spar = NULL,
                  cv = FALSE, all.knots = FALSE, nknots = NULL,
                  df.offset = 0, penalty = 1, control.spar = list())

### Arguments

`x` | a vector giving the values of the predictor variable, or a list or a two-column matrix specifying x and y.
`y` | responses. If `y` is missing, the responses are assumed to be specified by `x`.
`w` | optional vector of weights of the same length as `x`; defaults to all 1.
`df` | the desired equivalent number of degrees of freedom (trace of the smoother matrix).
`spar` | smoothing parameter, typically (but not necessarily) in (0,1]. The coefficient λ of the integral of the squared second derivative in the fit (penalized log likelihood) criterion is a monotone function of `spar`, see the details below.
`cv` | ordinary (`TRUE`) or “generalized” cross-validation (GCV) when `FALSE`.
`all.knots` | if `TRUE`, all distinct points in `x` are used as knots. If `FALSE` (default), a subset of `x[]` is used, specifically `x[j]` where the `nknots` indices are evenly spaced in `1:n`, see also the next argument `nknots`.
`nknots` | integer giving the number of knots to use when `all.knots=FALSE`. Per default, this is less than n, the number of unique `x` values for n > 49.
`df.offset` | allows the degrees of freedom to be increased by `df.offset` in the GCV criterion.
`penalty` | the coefficient of the penalty for degrees of freedom in the GCV criterion.

`control.spar`

optional list with named components controlling the root finding when the smoothing parameter `spar` is computed, i.e., missing or `NULL`, see below.
Note that this is partly experimental and may change with general spar computation improvements!

low:
  lower bound for `spar`; defaults to -1.5 (used to implicitly default to 0 in R versions earlier than 1.4).
high:
  upper bound for `spar`; defaults to +1.5.
tol:
  the absolute precision (tolerance) used; defaults to 1e-4 (formerly 1e-3).
eps:
  the relative precision used; defaults to 2e-8 (formerly 0.00244).
trace:
  logical indicating if iterations should be traced.
maxit:
  integer giving the maximal number of iterations; defaults to 500.

Note that `spar` is only searched for in the interval [low, high].

### Details

The `x` vector should contain at least four distinct values. Distinct here means “distinct after rounding to 6 significant digits”, i.e., `x` will be transformed to `unique(sort(signif(x, 6)))`, and `y` and `w` are pooled accordingly.

The computational λ used (as a function of `spar`) is lambda = r * 256^(3*spar - 1) where r = tr(X' W X) / tr(Σ), Σ is the matrix given by Sigma[i,j] = Integral B''[i](t) B''[j](t) dt, X is given by X[i,j] = B[j](x[i]), W is the diagonal matrix of weights (scaled such that its trace is n, the original number of observations) and B[k](.) is the k-th B-spline.

Note that with these definitions, f_i = f(x_i), and the B-spline basis representation f = X c (i.e., c is the vector of spline coefficients), the penalized log likelihood is L = (y - f)' W (y - f) + λ c' Σ c, and hence c is the solution of the (ridge regression) (X' W X + λ Σ) c = X' W y.

If `spar` is missing or `NULL`, the value of `df` is used to determine the degree of smoothing. If both are missing, leave-one-out cross-validation (ordinary or “generalized” as determined by `cv`) is used to determine λ. Note that from the above relation, `spar` is spar = s0 + 0.0601 * log(lambda), which is intentionally different from the S-plus implementation of `smooth.spline` (where `spar` is proportional to λ). In R's (log λ) scale, it makes more sense to vary `spar` linearly.

Note however that currently the results may become very unreliable for `spar` values smaller than about -1 or -2. The same may happen for values larger than 2 or so. Don't think of setting `spar` or the controls `low` and `high` outside such a safe range, unless you know what you are doing!

The “generalized” cross-validation method will work correctly when there are duplicated points in `x`. However, it is ambiguous what leave-one-out cross-validation means with duplicated points, and the internal code uses an approximation that involves leaving out groups of duplicated points. `cv=TRUE` is best avoided in that case.

### Value

An object of class `"smooth.spline"` with components

`x` | the distinct `x` values in increasing order, see the Details above.
`y` | the fitted values corresponding to `x`.
`w` | the weights used at the unique values of `x`.
`yin` | the y values used at the unique `y` values.
`lev` | leverages, the diagonal values of the smoother matrix.
`cv.crit` | cross-validation score, “generalized” or true, depending on `cv`.
`pen.crit` | penalized criterion
`crit` | the criterion value minimized in the underlying `.Fortran` routine ‘`sslvrg`’.
`df` | equivalent degrees of freedom used. Note that (currently) this value may become quite unprecise when the true `df` is between and 1 and 2.
`spar` | the value of `spar` computed or given.
`lambda` | the value of λ corresponding to `spar`, see the details above.
`iparms` | named integer(3) vector where `..$ipars["iter"]` gives number of spar computing iterations used.

`fit`

list for use by `predict.smooth.spline`, with components

knot:
  the knot sequence (including the repeated boundary knots).
nk:
  number of coefficients or number of “proper” knots plus 2.
coef:
  coefficients for the spline basis used.
min, range:
  numbers giving the corresponding quantities of `x`.

`call` | the matched call.

### Note

The default `all.knots = FALSE` and `nknots = NULL` entails using only O(n^{0.2}) knots instead of n for n > 49. This cuts speed and memory requirements, but not drastically anymore since R version 1.5.1 where it is only O(nk) + O(n) where nk is the number of knots. In this case where not all unique `x` values are used as knots, the result is not a smoothing spline in the strict sense, but very close unless a small smoothing parameter (or large `df`) is used.

### Author(s)

R implementation by B. D. Ripley and Martin Maechler (`spar/lambda`, etc).

This function is based on code in the `GAMFIT` Fortran program by T. Hastie and R. Tibshirani (http://lib.stat.cmu.edu/general/), which makes use of spline code by Finbarr O'Sullivan. Its design parallels the `smooth.spline` function of Chambers & Hastie (1992).

### References

Chambers, J. M. and Hastie, T. J. (1992) Statistical Models in S, Wadsworth & Brooks/Cole.

Green, P. J. and Silverman, B. W. (1994) Nonparametric Regression and Generalized Linear Models: A Roughness Penalty Approach. Chapman and Hall.

Hastie, T. J. and Tibshirani, R. J. (1990) Generalized Additive Models. Chapman and Hall.

### See Also

`predict.smooth.spline` for evaluating the spline and its derivatives.

### Examples

    data(cars)
    attach(cars)
    plot(speed, dist, main = "data(cars)  &  smoothing splines")
    cars.spl <- smooth.spline(speed, dist)
    (cars.spl)
    ## This example has duplicate points, so avoid cv=TRUE

    lines(cars.spl, col = "blue")
    lines(smooth.spline(speed, dist, df=10), lty=2, col = "red")
    legend(5,120,c(paste("default [C.V.] => df =",round(cars.spl$df,1)),
                   "s( * , df = 10)"), col = c("blue","red"), lty = 1:2,
           bg='bisque')
    detach()

    ##-- artificial example
    y18 <- c(1:3,5,4,7:3,2*(2:5),rep(10,4))
    xx  <- seq(1,length(y18), len=201)
    (s2  <- smooth.spline(y18)) # GCV
    (s02 <- smooth.spline(y18, spar = 0.2))
    plot(y18, main=deparse(s2$call), col.main=2)
    lines(s2, col = "gray"); lines(predict(s2, xx), col = 2)
    lines(predict(s02, xx), col = 3); mtext(deparse(s02$call), col = 3)

    ## The following shows the problematic behavior of 'spar' searching:
    (s2  <- smooth.spline(y18,          con=list(trace=TRUE,tol=1e-6, low= -1.5)))
    (s2m <- smooth.spline(y18, cv=TRUE, con=list(trace=TRUE,tol=1e-6, low= -1.5)))
    ## both above do quite similarly (Df = 8.5 +- 0.2)

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